What is Artificial Intelligence (AI)?
Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with the simulation of human intelligence processes using complex algorithms and mathematical functions. Thanks to AI we can build smart machines that can adapt, reason, and provide solutions, allowing them to model, and even improve upon, the capabilities of the human mind.
How does AI work?
To better understand how AI works, we need to get a grasp of what Machine Learning (ML) is. Machine learning is a technique to achieve AI, it provides a machine with the capability to learn from data and experience through algorithms. This means that through ML, machines can perceive data and patterns in a way similar to human beings, throughout their lifetime.
In general, AI systems work by ingesting large amounts of labelled training data, analysing the data for correlations and patterns, and using these patterns to make predictions about future states. In this way, a chatbot that is fed with examples of text chats can learn to produce lifelike exchanges with people, or an image recognition tool can learn to identify, describe, and separate objects in images, like for example healthy and diseased vineyards, reviewing millions of images of such plants.
As AI continues to develop at an exponential rate, it is transforming the business world. At this point, AI is used everywhere around us, in our smartphones and all kinds of intelligent devices we use in our everyday lives, to the advertisement selection when you click on a video to watch or even the hospitals where AI-assisted diagnosis is all but a common practice. Other use cases of AI include expert systems, natural language processing, speech recognition, machine vision, and even their combinations in such applications as self-driving cars or robots amongst many others. As a result, many tech companies across various industries are investing in artificially intelligent technologies.
Use of AI in Future Needs EU Projects
If you want to learn more about the practical implementations of such tools like the aforementioned, you can follow the progress of our brand-new projects, such as:
- The ERDF-funded project SmartVitiNet, where we aim to introduce these innovative technologies to combat the effects of climate change and diseases on the vineyards.
- The Horizon Europe funded REFMAP, where ground-breaking AI techniques will be used to enable the quantification and (eventually) reduction of the environmental impact of aviation from a multimodal perspective.
- The Horizon Europe funded HiDALGO2, which aims to explore synergies between modelling, data acquisition, simulation, data analysis, and visualisation along with achieving better scalability on current and future HPC and AI infrastructures to deliver highly-scalable solutions that can effectively utilise pre-exascale systems, tackling climate change.
What are the current bottlenecks in its implementation?
These new technologies will help solve some of the most challenging problems of our age but what the tech optimists tend to leave out is that our path to the bright, shiny AI future has some major potholes in it. One looming problem that is really large is that right now, AI and ML don’t scale well.
The ability to expand a single machine’s capability to broader, more widespread applications is the main challenge nowadays and while algorithms may provide a solution in the future when it comes to creating them, we’re currently stuck in a painstaking, brute force methodology.
Left to their own devices, AI will suck up as much time, computing, and data/bandwidth as you give it, which leads us to another issue, sustainability. Processing, transfer, storage, and other data manipulations consume vast amounts of energy and by extension produce greenhouse gas emissions which contribute significantly to climate change. Constantly feeding the machines with never-ending chunks of data for processing, is causing often completely unnecessary disturbances to an already fragile ecosystem.
Having access to such huge data amounts also raises security concerns. Either they are data privacy-related or intentional data manipulation for the sake of interfering with the outcome of the AI, such issues are not to be taken lightly. Both security and sustainability issues have caught the attention of the European Commission, which launched last year several calls for projects to address them.
Future Needs is very proud to be part of one such project, named TANGO. TANGO’s value proposition is tied to the design, development, and deployment of novel Artificial Intelligence (AI) and Machine Learning (ML) techniques for environmentally sustainable data operations. The proposed solutions have been designed to optimise the trade-off among energy consumption, privacy, data transfer, and security. TANGO’s impact and adoption will be accelerated by the energy-efficient training of AI models considering the constraints of edge computing devices. Moreover, AI and ML methods will be employed to generate alerts and recommendations aiming to mitigate privacy and security risks. Explainable AI (X-AI) mechanisms will reveal to end users the logic followed by the AI algorithms to reach certain conclusions supporting decisions about green and responsible operations. TANGO also introduces a novel AI-powered seamless onboarding mechanism for users and devices compatible with distributed identity and trust management offering high security and privacy as there is no human intervention in the process. It will provide the only system that introduces a self-compensating continuous authentication system that autonomously learns the users’ behavioural patterns. TANGO will be the only system that offers a) continuous behavioural authentication that does not require any user input b) AI-powered authentication that combines 14 independent behavioural traits such as human and device behavioural patterns c) operates as primary and secondary continuous authentication that fully discards passwords.
The main advancements in the field of AI
AI is an interdisciplinary science with multiple approaches, but advancements in machine learning and deep learning are creating a paradigm shift in virtually every sector of the tech industry.
Some of the most significant advancements in the AI field include:
- Vaccine development: Machine learning models can sort through the tens of thousands of subcomponents of the outer proteins of a virus, and predict which ones are the most immunogenic – i.e., capable of producing an immune response – and thereby guide researchers in designing targeted vaccines. The use of AI in vaccine development may revolutionise the way all vaccines are created in the future.
- AI powered by quantum computing: The potential of quantum computing is limitless, but its integration into artificial intelligence will produce a technology that will be rather powerful than anything we have today. The new technology will enable machines to learn and self-evolve. It will make them exponentially better at solving complex problems and developing self-learning algorithms that will drive efficiency in sectors such as finance or healthcare. Quantum AI systems will be able to process large amounts of information quickly and accurately. This will open up a new world of possibilities for businesses and individuals. They will also be able to solve complex problems that are impossible for even the most advanced conventional computer systems.
- Analog computer architecture: With huge data, great computing power is needed for their analysis. Analog computing provides several key advantages when it comes to these computations. First, it is amazingly efficient; it eliminates memory movement for the neural network weights since they are used in place as resistors. Second, it is high performance; there are hundreds of thousands of multiply-accumulate operations occurring in parallel when performing one of these vector operations. So, with Moore’s law fast approaching its end, companies will have to find a different approach to make next-generation products that are high computation and low power, and analog computers might be the best one for the near future.
When one considers the computational costs and the technical data infrastructure running behind artificial intelligence, actually executing AI is a complex and costly business. Fortunately, there have been massive advancements in computing technology, as indicated by Moore’s Law (the law is attributed to Gordon Moore, the co-founder and former CEO of Intel), which states that the number of transistors on a microchip doubles about every two years while the cost of computers is halved.
It is obvious that there are many challenges and issues to be addressed in our struggle to achieve more in the AI field, thankfully, the scientific community does not rest, providing ingenious solutions at every step of the way.
Although many experts believe that Moore’s Law will likely come to an end sometime in the 2020s, this has had a major impact on modern AI techniques – without it, deep learning would be out of the question, financially speaking. Recent research found that AI innovation has actually outperformed Moore’s Law, doubling every six months or so as opposed to two years.
Thinking of the achievements of AI so far, we understand that the advancements made across a variety of industries have been major over the last several years. And the potential for an even greater impact over the next several decades seems all but inevitable.
The Future of AI
Artificial intelligence is shaping the future of humanity across nearly every industry. It is already the main driver of emerging technologies like big data, robotics, and IoT, and it will continue to act as a technological innovator for the foreseeable future.
With AI and machine learning (ML), we can expect to see orders of magnitude of improvement in what can be accomplished. There’s a certain set of ideas that humans can computationally explore. There’s a broader set of ideas that humans with computers can address. And there’s a much bigger set of ideas that humans with computers, plus AI, can successfully tackle. AI enables an unprecedented ability to analyse enormous data sets and computationally discover complex relationships and patterns. AI, augmenting human intelligence, is primed to transform the scientific research process, unleashing a new golden age of scientific discovery in the coming years.